讲座题目：
1. Research Across Cognition, Healthcare, and Human-Computer Interaction (HCI)
2. Dancing with the Production Planning System: Human Planners under Uncertainty and Infeasible Suggestion
主讲嘉宾：1. Julija Vaitonytė 2. 谭丽佳（Lijia Tan）
时间：2026年7月16日14:00--16:00
地点：商学院118利安达厅
欢迎广大师生参加！
江南大学商学院
2026年7月14日
主讲嘉宾简介
1. Dr. Julija Vaitonytė is a Lecturer in the Department of Computational Cognitive Science at Tilburg University. She successfully defended her doctoral dissertation in January 2024, titled The Face Puzzle: Decoding Human Perception of Digital Agents, which systematically explored how humans perceive virtual digital agents.
In her research, she applies neuroscientific methods such as EEG, behavioral experiments, and computational modeling of human data. She has published multiple research outputs covering human-agent interaction, trust in virtual agents, and the neural mechanisms of the uncanny valley. Her work has been presented at top academic conferences including the ACM International Conference on Intelligent Virtual Agents (IVA) and the Annual Meeting of the Cognitive Science Society, as well as published in international journals such as Computers in Human Behavior Reports and Language and Cognition. Her research integrates Cognitive Science, Neuroscience, and Artificial Intelligence, and her findings have provided theoretical references for the development of virtual interaction systems in healthcare and education, helping to optimize the interaction design of virtual agents.
Currently, she is investigating the social impact of Large Language Models on human behavior, as part of a research grant awarded to her at the end of 2025. In terms of teaching, she is responsible for courses such as Introduction to Cognitive Science and the Research Workshop for Computational Cognitive Science, and also supervises master students' thesis work.
2. Dr. Lijia Tan has been an Assistant Professor at Eindhoven University of Technology (TU/e) since 2020, where she is a member of the Operations, Planning, Accounting, and Control (OPAC) research group within the Department of Industrial Engineering & Innovation Sciences.
She obtained her PhD in Economics from Xiamen University, China, in 2015. After graduation, she was funded by the Fritz Thyssen Stiftung to conduct two years of postdoctoral research at the University of Cologne in Germany. She then moved to the Netherlands, working as a postdoctoral researcher in the OPAC research group at Eindhoven University of Technology until 2019. Before returning to take up her current position at TU/e, she also worked at Tianjin University in China.
Her research focuses on Behavioral Operations Management. She uses laboratory experiments to observe human decision-making behavior, and tests behavioral models in operations management based on relevant data. Her research topics cover human decision-making under uncertainty, supply chain management, auction mechanisms, as well as AI-supported planning and decision support systems. Her recent work examines the interaction mechanisms between human judgment and analytical/AI-based systems in operational settings.
Her research has been published in top international journals in the fields of operations management and economics, including Management Science, Production and Operations Management, European Journal of Operational Research, and China Economic Review.
讲座主要内容
1. This presentation showcases several research projects spanning cognition, healthcare, and human-computer interaction. I will introduce machine learning methods for assessing cognitive performance from behavioral and physiological data, summarize findings from a review of large language model (LLM) adoption in healthcare, and discuss some of my current and upcoming HCI studies examining how people interact with LLM-based systems. These examples highlight the value of interdisciplinary research in developing AI technologies that better understand and support human needs.
2. In manufacturing operations, production planning involves a set of critical decisions. These decisions must be made by human planners but are usually supported by production planning systems. While system developers typically evaluate systems based on the quality of their suggested solutions only, human planners consider more aspects. We adopt a behavioral perspective to assess the effectiveness and efficiency of production planning systems by examining how they influence human decision-making. We focus on two major challenges faced by production planners, feasibility constraints and demand uncertainty. We conduct a laboratory experiment with three treatments: a Control system that addresses neither challenge, a Material Requirement Planning (MRP) system that addresses demand uncertainty only, and a Synchronized Base Stock (SBS) system that addresses both demand uncertainty and feasibility. The experimental results show that (1) systems that address demand uncertainty significantly improve decision performance by reducing total cost and shortening decision time, (2) the system that additionally addresses feasibility only improves performance to a limited extent, leaving total cost unaffected while slightly reducing decision time, and (3) system type has a limited effect on the performance of the highest-performing participants but substantially affects the remaining participants, particularly the lowest-performing ones. We assess the robustness of these results by conducting the experiment with practitioners. Our study provides behavioral evidence to inform the ongoing innovation of production planning systems, highlighting which challenges should be prioritized to most effectively enhance planners’ decision performance.
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